Overview

Dataset statistics

Number of variables44
Number of observations20000
Missing cells286735
Missing cells (%)32.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory352.0 B

Variable types

Numeric39
Categorical5

Alerts

SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Unit1 is highly correlated with Unit2High correlation
EtCO2 has 16784 (83.9%) missing values Missing
BaseExcess has 19442 (97.2%) missing values Missing
HCO3 has 19584 (97.9%) missing values Missing
FiO2 has 14178 (70.9%) missing values Missing
pH has 14246 (71.2%) missing values Missing
PaCO2 has 14221 (71.1%) missing values Missing
SaO2 has 14875 (74.4%) missing values Missing
AST has 11536 (57.7%) missing values Missing
BUN has 1591 (8.0%) missing values Missing
Alkalinephos has 11530 (57.6%) missing values Missing
Calcium has 1550 (7.8%) missing values Missing
Chloride has 18383 (91.9%) missing values Missing
Creatinine has 1588 (7.9%) missing values Missing
Bilirubin_direct has 18529 (92.6%) missing values Missing
Glucose has 1173 (5.9%) missing values Missing
Lactate has 15240 (76.2%) missing values Missing
Magnesium has 3543 (17.7%) missing values Missing
Phosphate has 8365 (41.8%) missing values Missing
Potassium has 1434 (7.2%) missing values Missing
Bilirubin_total has 11522 (57.6%) missing values Missing
TroponinI has 13436 (67.2%) missing values Missing
Hct has 1953 (9.8%) missing values Missing
Hgb has 1941 (9.7%) missing values Missing
PTT has 15602 (78.0%) missing values Missing
WBC has 2000 (10.0%) missing values Missing
Fibrinogen has 18052 (90.3%) missing values Missing
Platelets has 1992 (10.0%) missing values Missing
Unit1 has 6095 (30.5%) missing values Missing
Unit2 has 6095 (30.5%) missing values Missing
FiO2 is highly skewed (γ1 = 76.2985385) Skewed
PatientID is uniformly distributed Uniform
PatientID has unique values Unique
HospAdmTime has 1145 (5.7%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:24:36.588632
Analysis finished2021-11-29 10:24:45.631614
Duration9.04 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110000.5
Minimum100001
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:45.679760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile101000.95
Q1105000.75
median110000.5
Q3115000.25
95-th percentile119000.05
Maximum120000
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.05248746167
Kurtosis-1.2
Mean110000.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum2200010000
Variance33335000
MonotonicityStrictly increasing
2021-11-29T11:24:45.786563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000011
 
< 0.1%
1133311
 
< 0.1%
1133381
 
< 0.1%
1133371
 
< 0.1%
1133361
 
< 0.1%
1133351
 
< 0.1%
1133341
 
< 0.1%
1133331
 
< 0.1%
1133321
 
< 0.1%
1133301
 
< 0.1%
Other values (19990)19990
> 99.9%
ValueCountFrequency (%)
1000011
< 0.1%
1000021
< 0.1%
1000031
< 0.1%
1000041
< 0.1%
1000051
< 0.1%
1000061
< 0.1%
1000071
< 0.1%
1000081
< 0.1%
1000091
< 0.1%
1000101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

Distinct250
Distinct (%)1.3%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean101.0054761
Minimum42
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:45.975474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile72
Q187
median100
Q3113
95-th percentile135.625
Maximum211
Range169
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.5572884
Coefficient of variation (CV)0.1936260206
Kurtosis0.3146285639
Mean101.0054761
Median Absolute Deviation (MAD)13
Skewness0.4659869627
Sum2019705.5
Variance382.4875294
MonotonicityNot monotonic
2021-11-29T11:24:46.072715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90541
 
2.7%
96499
 
2.5%
100488
 
2.4%
92479
 
2.4%
102458
 
2.3%
94453
 
2.3%
104444
 
2.2%
98435
 
2.2%
106409
 
2.0%
84405
 
2.0%
Other values (240)15385
76.9%
ValueCountFrequency (%)
421
 
< 0.1%
441
 
< 0.1%
451
 
< 0.1%
482
 
< 0.1%
492
 
< 0.1%
502
 
< 0.1%
514
 
< 0.1%
527
< 0.1%
532
 
< 0.1%
5411
0.1%
ValueCountFrequency (%)
2111
 
< 0.1%
1941
 
< 0.1%
1912
< 0.1%
1862
< 0.1%
1842
< 0.1%
182.51
 
< 0.1%
1822
< 0.1%
181.51
 
< 0.1%
1811
 
< 0.1%
1803
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct29
Distinct (%)0.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99.49494848
Minimum74
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:46.165512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile97
Q199
median100
Q3100
95-th percentile100
Maximum100
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.074129091
Coefficient of variation (CV)0.01079581534
Kurtosis49.7162905
Mean99.49494848
Median Absolute Deviation (MAD)0
Skewness-4.482165863
Sum1989302
Variance1.153753304
MonotonicityNot monotonic
2021-11-29T11:24:46.251129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10014526
72.6%
992416
 
12.1%
981529
 
7.6%
97650
 
3.2%
96243
 
1.2%
99.5209
 
1.0%
98.5140
 
0.7%
9588
 
0.4%
97.576
 
0.4%
96.539
 
0.2%
Other values (19)78
 
0.4%
ValueCountFrequency (%)
741
 
< 0.1%
791
 
< 0.1%
801
 
< 0.1%
821
 
< 0.1%
831
 
< 0.1%
841
 
< 0.1%
852
< 0.1%
863
< 0.1%
871
 
< 0.1%
892
< 0.1%
ValueCountFrequency (%)
10014526
72.6%
99.5209
 
1.0%
992416
 
12.1%
98.5140
 
0.7%
981529
 
7.6%
97.576
 
0.4%
97650
 
3.2%
96.539
 
0.2%
96243
 
1.2%
95.513
 
0.1%

Temp
Real number (ℝ≥0)

Distinct117
Distinct (%)0.6%
Missing49
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.35684928
Minimum32.6
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:46.345727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum32.6
5-th percentile36.4
Q136.9
median37.3
Q337.8
95-th percentile38.6
Maximum50
Range17.4
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7075059994
Coefficient of variation (CV)0.01893912396
Kurtosis11.62398671
Mean37.35684928
Median Absolute Deviation (MAD)0.4
Skewness1.204899895
Sum745306.5
Variance0.5005647391
MonotonicityNot monotonic
2021-11-29T11:24:46.443752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
371537
 
7.7%
37.21302
 
6.5%
36.81287
 
6.4%
37.11211
 
6.1%
36.91089
 
5.4%
37.41058
 
5.3%
37.51024
 
5.1%
37.61008
 
5.0%
37.31003
 
5.0%
36.7925
 
4.6%
Other values (107)8507
42.5%
ValueCountFrequency (%)
32.61
 
< 0.1%
32.81
 
< 0.1%
33.31
 
< 0.1%
33.51
 
< 0.1%
342
< 0.1%
34.11
 
< 0.1%
34.41
 
< 0.1%
34.51
 
< 0.1%
34.91
 
< 0.1%
353
< 0.1%
ValueCountFrequency (%)
502
< 0.1%
42.11
< 0.1%
41.81
< 0.1%
41.51
< 0.1%
41.42
< 0.1%
41.31
< 0.1%
41.251
< 0.1%
411
< 0.1%
40.91
< 0.1%
40.81
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct371
Distinct (%)1.9%
Missing24
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean157.0853599
Minimum66
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:46.545030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile117
Q1138
median155
Q3174
95-th percentile203
Maximum300
Range234
Interquartile range (IQR)36

Descriptive statistics

Standard deviation26.80667552
Coefficient of variation (CV)0.1706503746
Kurtosis0.8191790347
Mean157.0853599
Median Absolute Deviation (MAD)18
Skewness0.5269478231
Sum3137937.15
Variance718.5978525
MonotonicityNot monotonic
2021-11-29T11:24:46.644144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140344
 
1.7%
156318
 
1.6%
158317
 
1.6%
150314
 
1.6%
164312
 
1.6%
154306
 
1.5%
146306
 
1.5%
148298
 
1.5%
168297
 
1.5%
152293
 
1.5%
Other values (361)16871
84.4%
ValueCountFrequency (%)
662
< 0.1%
701
 
< 0.1%
721
 
< 0.1%
741
 
< 0.1%
751
 
< 0.1%
761
 
< 0.1%
771
 
< 0.1%
791
 
< 0.1%
801
 
< 0.1%
823
< 0.1%
ValueCountFrequency (%)
3001
 
< 0.1%
2991
 
< 0.1%
2981
 
< 0.1%
2963
< 0.1%
2951
 
< 0.1%
2941
 
< 0.1%
2932
< 0.1%
2922
< 0.1%
2902
< 0.1%
2871
 
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct343
Distinct (%)1.7%
Missing102
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean110.3982561
Minimum43.5
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:46.742708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum43.5
5-th percentile83
Q196
median107
Q3120
95-th percentile146
Maximum300
Range256.5
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.46768543
Coefficient of variation (CV)0.2125729723
Kurtosis13.67664927
Mean110.3982561
Median Absolute Deviation (MAD)12
Skewness2.536329695
Sum2196704.5
Variance550.7322596
MonotonicityNot monotonic
2021-11-29T11:24:46.842295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100468
 
2.3%
104460
 
2.3%
98455
 
2.3%
96447
 
2.2%
102445
 
2.2%
106434
 
2.2%
108426
 
2.1%
105423
 
2.1%
110423
 
2.1%
103417
 
2.1%
Other values (333)15500
77.5%
ValueCountFrequency (%)
43.51
 
< 0.1%
553
< 0.1%
571
 
< 0.1%
581
 
< 0.1%
602
 
< 0.1%
611
 
< 0.1%
623
< 0.1%
62.51
 
< 0.1%
635
< 0.1%
642
 
< 0.1%
ValueCountFrequency (%)
3004
< 0.1%
2985
< 0.1%
2963
< 0.1%
2951
 
< 0.1%
2944
< 0.1%
2932
 
< 0.1%
2922
 
< 0.1%
2912
 
< 0.1%
2903
< 0.1%
2882
 
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct332
Distinct (%)1.7%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean88.14609273
Minimum32
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:46.941518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile64
Q175.5
median85
Q397
95-th percentile120
Maximum300
Range268
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation20.65945875
Coefficient of variation (CV)0.2343774762
Kurtosis19.22165676
Mean88.14609273
Median Absolute Deviation (MAD)11
Skewness2.861997187
Sum1760541.91
Variance426.8132359
MonotonicityNot monotonic
2021-11-29T11:24:47.042168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80544
 
2.7%
84535
 
2.7%
82529
 
2.6%
76521
 
2.6%
78511
 
2.6%
86486
 
2.4%
81468
 
2.3%
79467
 
2.3%
88460
 
2.3%
90454
 
2.3%
Other values (322)14998
75.0%
ValueCountFrequency (%)
321
 
< 0.1%
362
 
< 0.1%
411
 
< 0.1%
41.51
 
< 0.1%
421
 
< 0.1%
453
< 0.1%
45.51
 
< 0.1%
467
< 0.1%
471
 
< 0.1%
47.51
 
< 0.1%
ValueCountFrequency (%)
3002
< 0.1%
2981
 
< 0.1%
2963
< 0.1%
2932
< 0.1%
2922
< 0.1%
2911
 
< 0.1%
2902
< 0.1%
2871
 
< 0.1%
2851
 
< 0.1%
2841
 
< 0.1%

Resp
Real number (ℝ≥0)

Distinct137
Distinct (%)0.7%
Missing43
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean25.45011775
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:47.142908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q122
median24
Q328
95-th percentile36
Maximum100
Range99
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.703606788
Coefficient of variation (CV)0.2634017985
Kurtosis28.77288584
Mean25.45011775
Median Absolute Deviation (MAD)3
Skewness3.409856074
Sum507908
Variance44.93834397
MonotonicityNot monotonic
2021-11-29T11:24:47.242063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242129
 
10.6%
221979
 
9.9%
201625
 
8.1%
261433
 
7.2%
251393
 
7.0%
281379
 
6.9%
181214
 
6.1%
231063
 
5.3%
30964
 
4.8%
21825
 
4.1%
Other values (127)5953
29.8%
ValueCountFrequency (%)
14
< 0.1%
1.51
 
< 0.1%
29
< 0.1%
36
< 0.1%
45
< 0.1%
4.51
 
< 0.1%
58
< 0.1%
65
< 0.1%
6.51
 
< 0.1%
73
 
< 0.1%
ValueCountFrequency (%)
1005
< 0.1%
997
< 0.1%
986
< 0.1%
975
< 0.1%
96.51
 
< 0.1%
964
< 0.1%
952
 
< 0.1%
943
< 0.1%
932
 
< 0.1%
921
 
< 0.1%

EtCO2
Real number (ℝ≥0)

MISSING

Distinct124
Distinct (%)3.9%
Missing16784
Missing (%)83.9%
Infinite0
Infinite (%)0.0%
Mean38.2994403
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:47.416755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile22
Q133
median38
Q343
95-th percentile52
Maximum100
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.25899176
Coefficient of variation (CV)0.2939727493
Kurtosis9.750683958
Mean38.2994403
Median Absolute Deviation (MAD)5
Skewness1.980621967
Sum123171
Variance126.7648955
MonotonicityNot monotonic
2021-11-29T11:24:47.510885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38149
 
0.7%
41147
 
0.7%
37140
 
0.7%
36139
 
0.7%
42137
 
0.7%
40135
 
0.7%
39135
 
0.7%
35130
 
0.7%
44111
 
0.6%
34106
 
0.5%
Other values (114)1887
 
9.4%
(Missing)16784
83.9%
ValueCountFrequency (%)
104
< 0.1%
10.55
< 0.1%
117
< 0.1%
126
< 0.1%
12.51
 
< 0.1%
134
< 0.1%
13.53
< 0.1%
145
< 0.1%
14.53
< 0.1%
154
< 0.1%
ValueCountFrequency (%)
10010
0.1%
994
 
< 0.1%
9810
0.1%
979
< 0.1%
963
 
< 0.1%
952
 
< 0.1%
942
 
< 0.1%
932
 
< 0.1%
924
 
< 0.1%
861
 
< 0.1%

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct189
Distinct (%)33.9%
Missing19442
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean-1.143996416
Minimum-21.8
Maximum14.2
Zeros4
Zeros (%)< 0.1%
Negative333
Negative (%)1.7%
Memory size156.4 KiB
2021-11-29T11:24:47.609150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-21.8
5-th percentile-8.0225
Q1-3.4
median-0.9
Q31.4
95-th percentile5.4
Maximum14.2
Range36
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.214126748
Coefficient of variation (CV)-3.683688769
Kurtosis2.166493674
Mean-1.143996416
Median Absolute Deviation (MAD)2.4
Skewness-0.4150854249
Sum-638.35
Variance17.75886425
MonotonicityNot monotonic
2021-11-29T11:24:47.705243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.513
 
0.1%
-1.110
 
0.1%
-3.49
 
< 0.1%
-0.59
 
< 0.1%
-4.48
 
< 0.1%
1.58
 
< 0.1%
1.48
 
< 0.1%
-1.38
 
< 0.1%
-0.98
 
< 0.1%
-0.67
 
< 0.1%
Other values (179)470
 
2.4%
(Missing)19442
97.2%
ValueCountFrequency (%)
-21.81
< 0.1%
-18.251
< 0.1%
-15.651
< 0.1%
-15.11
< 0.1%
-14.351
< 0.1%
-13.851
< 0.1%
-12.61
< 0.1%
-11.81
< 0.1%
-11.41
< 0.1%
-11.32
< 0.1%
ValueCountFrequency (%)
14.21
 
< 0.1%
13.31
 
< 0.1%
11.11
 
< 0.1%
10.21
 
< 0.1%
9.11
 
< 0.1%
91
 
< 0.1%
8.51
 
< 0.1%
8.41
 
< 0.1%
7.91
 
< 0.1%
7.63
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct145
Distinct (%)34.9%
Missing19584
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean24.56983173
Minimum7.7
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:47.803499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile18.9
Q122.6
median24.85
Q326.4125
95-th percentile29.75
Maximum36.4
Range28.7
Interquartile range (IQR)3.8125

Descriptive statistics

Standard deviation3.360838926
Coefficient of variation (CV)0.1367872179
Kurtosis2.24279663
Mean24.56983173
Median Absolute Deviation (MAD)1.875
Skewness-0.3018458229
Sum10221.05
Variance11.29523828
MonotonicityNot monotonic
2021-11-29T11:24:47.896930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.313
 
0.1%
23.511
 
0.1%
24.510
 
0.1%
23.29
 
< 0.1%
25.99
 
< 0.1%
26.19
 
< 0.1%
269
 
< 0.1%
257
 
< 0.1%
24.67
 
< 0.1%
26.57
 
< 0.1%
Other values (135)325
 
1.6%
(Missing)19584
97.9%
ValueCountFrequency (%)
7.71
< 0.1%
13.11
< 0.1%
13.71
< 0.1%
151
< 0.1%
16.51
< 0.1%
16.81
< 0.1%
172
< 0.1%
17.31
< 0.1%
17.41
< 0.1%
17.51
< 0.1%
ValueCountFrequency (%)
36.41
< 0.1%
361
< 0.1%
35.31
< 0.1%
33.71
< 0.1%
32.91
< 0.1%
32.41
< 0.1%
322
< 0.1%
31.61
< 0.1%
31.11
< 0.1%
30.91
< 0.1%

FiO2
Real number (ℝ≥0)

MISSING
SKEWED

Distinct68
Distinct (%)1.2%
Missing14178
Missing (%)70.9%
Infinite0
Infinite (%)0.0%
Mean1.261176572
Minimum0.04
Maximum4000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:47.997576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.21
Q10.4
median0.5
Q30.8
95-th percentile1
Maximum4000
Range3999.96
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation52.41651778
Coefficient of variation (CV)41.56160125
Kurtosis5821.644562
Mean1.261176572
Median Absolute Deviation (MAD)0.2
Skewness76.2985385
Sum7342.57
Variance2747.491336
MonotonicityNot monotonic
2021-11-29T11:24:48.097212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11330
 
6.7%
0.41168
 
5.8%
0.5915
 
4.6%
0.21585
 
2.9%
0.7329
 
1.6%
0.6286
 
1.4%
0.28230
 
1.1%
0.3162
 
0.8%
0.8150
 
0.8%
0.35116
 
0.6%
Other values (58)551
 
2.8%
(Missing)14178
70.9%
ValueCountFrequency (%)
0.042
 
< 0.1%
0.054
 
< 0.1%
0.064
 
< 0.1%
0.21585
2.9%
0.2413
 
0.1%
0.258
 
< 0.1%
0.262
 
< 0.1%
0.271
 
< 0.1%
0.28230
 
1.1%
0.291
 
< 0.1%
ValueCountFrequency (%)
40001
 
< 0.1%
5.051
 
< 0.1%
214
 
0.1%
1.71
 
< 0.1%
1.41
 
< 0.1%
1.31
 
< 0.1%
1.23
 
< 0.1%
11330
6.7%
0.981
 
< 0.1%
0.971
 
< 0.1%

pH
Real number (ℝ≥0)

MISSING

Distinct63
Distinct (%)1.1%
Missing14246
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean7.407309698
Minimum6.81
Maximum7.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:48.203701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.81
5-th percentile7.3
Q17.36
median7.41
Q37.45
95-th percentile7.52
Maximum7.71
Range0.9
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.07246876263
Coefficient of variation (CV)0.009783412006
Kurtosis2.21484331
Mean7.407309698
Median Absolute Deviation (MAD)0.05
Skewness-0.2612562861
Sum42621.66
Variance0.005251721557
MonotonicityNot monotonic
2021-11-29T11:24:48.299412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.42371
 
1.9%
7.38368
 
1.8%
7.4364
 
1.8%
7.39316
 
1.6%
7.36309
 
1.5%
7.44300
 
1.5%
7.41293
 
1.5%
7.46275
 
1.4%
7.37269
 
1.3%
7.45266
 
1.3%
Other values (53)2623
 
13.1%
(Missing)14246
71.2%
ValueCountFrequency (%)
6.811
 
< 0.1%
6.851
 
< 0.1%
7.051
 
< 0.1%
7.061
 
< 0.1%
7.071
 
< 0.1%
7.091
 
< 0.1%
7.11
 
< 0.1%
7.111
 
< 0.1%
7.124
< 0.1%
7.142
< 0.1%
ValueCountFrequency (%)
7.711
 
< 0.1%
7.691
 
< 0.1%
7.681
 
< 0.1%
7.642
 
< 0.1%
7.634
 
< 0.1%
7.626
 
< 0.1%
7.6112
0.1%
7.612
0.1%
7.5920
0.1%
7.5822
0.1%

PaCO2
Real number (ℝ≥0)

MISSING

Distinct423
Distinct (%)7.3%
Missing14221
Missing (%)71.1%
Infinite0
Infinite (%)0.0%
Mean43.41301263
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:48.400277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile29.5
Q136.8
median41.6
Q347
95-th percentile66.2
Maximum100
Range88
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation11.53500494
Coefficient of variation (CV)0.2657038579
Kurtosis4.826518059
Mean43.41301263
Median Absolute Deviation (MAD)5.4
Skewness1.741863092
Sum250883.8
Variance133.0563389
MonotonicityNot monotonic
2021-11-29T11:24:48.493618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40256
 
1.3%
42252
 
1.3%
38227
 
1.1%
36212
 
1.1%
44204
 
1.0%
39189
 
0.9%
41186
 
0.9%
43186
 
0.9%
34177
 
0.9%
46176
 
0.9%
Other values (413)3714
 
18.6%
(Missing)14221
71.1%
ValueCountFrequency (%)
121
 
< 0.1%
153
< 0.1%
15.31
 
< 0.1%
162
< 0.1%
16.71
 
< 0.1%
173
< 0.1%
184
< 0.1%
18.81
 
< 0.1%
193
< 0.1%
19.21
 
< 0.1%
ValueCountFrequency (%)
1006
< 0.1%
994
< 0.1%
986
< 0.1%
975
< 0.1%
962
 
< 0.1%
952
 
< 0.1%
944
< 0.1%
93.41
 
< 0.1%
934
< 0.1%
92.51
 
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct187
Distinct (%)3.6%
Missing14875
Missing (%)74.4%
Infinite0
Infinite (%)0.0%
Mean97.51632195
Minimum50.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:48.588438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50.3
5-th percentile93.1
Q196.8
median98.3
Q399.2
95-th percentile99.7
Maximum100
Range49.7
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.842030854
Coefficient of variation (CV)0.02914415554
Kurtosis54.10734636
Mean97.51632195
Median Absolute Deviation (MAD)1
Skewness-5.276013539
Sum499771.15
Variance8.077139377
MonotonicityNot monotonic
2021-11-29T11:24:48.689301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.4203
 
1.0%
99.2196
 
1.0%
99.5185
 
0.9%
99.6180
 
0.9%
99174
 
0.9%
98.8165
 
0.8%
98.9161
 
0.8%
99.7160
 
0.8%
98.6156
 
0.8%
99.3155
 
0.8%
Other values (177)3390
 
17.0%
(Missing)14875
74.4%
ValueCountFrequency (%)
50.31
< 0.1%
52.51
< 0.1%
58.81
< 0.1%
65.11
< 0.1%
65.61
< 0.1%
681
< 0.1%
68.21
< 0.1%
70.81
< 0.1%
72.61
< 0.1%
73.31
< 0.1%
ValueCountFrequency (%)
1007
 
< 0.1%
99.972
 
0.4%
99.851
 
< 0.1%
99.8132
0.7%
99.753
 
< 0.1%
99.7160
0.8%
99.6180
0.9%
99.552
 
< 0.1%
99.5185
0.9%
99.451
 
< 0.1%

AST
Real number (ℝ≥0)

MISSING

Distinct717
Distinct (%)8.5%
Missing11536
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean137.2675449
Minimum5
Maximum9961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:48.866942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q120
median29
Q356
95-th percentile364.85
Maximum9961
Range9956
Interquartile range (IQR)36

Descriptive statistics

Standard deviation621.3371001
Coefficient of variation (CV)4.526467641
Kurtosis120.1149451
Mean137.2675449
Median Absolute Deviation (MAD)12
Skewness10.16229717
Sum1161832.5
Variance386059.792
MonotonicityNot monotonic
2021-11-29T11:24:48.968725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17292
 
1.5%
21289
 
1.4%
20286
 
1.4%
18283
 
1.4%
19281
 
1.4%
22269
 
1.3%
16256
 
1.3%
23242
 
1.2%
24236
 
1.2%
15220
 
1.1%
Other values (707)5810
29.0%
(Missing)11536
57.7%
ValueCountFrequency (%)
52
 
< 0.1%
65
 
< 0.1%
79
 
< 0.1%
814
 
0.1%
935
 
0.2%
9.51
 
< 0.1%
1060
0.3%
11106
0.5%
12143
0.7%
13142
0.7%
ValueCountFrequency (%)
99611
< 0.1%
97471
< 0.1%
97101
< 0.1%
96021
< 0.1%
95821
< 0.1%
95201
< 0.1%
94891
< 0.1%
92441
< 0.1%
92291
< 0.1%
91851
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct208
Distinct (%)1.1%
Missing1591
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean23.73311967
Minimum1
Maximum268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:49.069406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q112
median17
Q328
95-th percentile64
Maximum268
Range267
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.10751106
Coefficient of variation (CV)0.8472342171
Kurtosis12.91482916
Mean23.73311967
Median Absolute Deviation (MAD)7
Skewness2.88575184
Sum436903
Variance404.3120011
MonotonicityNot monotonic
2021-11-29T11:24:49.167981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14925
 
4.6%
13911
 
4.6%
12880
 
4.4%
15877
 
4.4%
11851
 
4.3%
10787
 
3.9%
17765
 
3.8%
16750
 
3.8%
18680
 
3.4%
9660
 
3.3%
Other values (198)10323
51.6%
(Missing)1591
 
8.0%
ValueCountFrequency (%)
16
 
< 0.1%
221
 
0.1%
369
 
0.3%
4138
 
0.7%
4.52
 
< 0.1%
5236
1.2%
5.52
 
< 0.1%
6372
1.9%
6.51
 
< 0.1%
7477
2.4%
ValueCountFrequency (%)
2681
< 0.1%
2521
< 0.1%
2321
< 0.1%
2271
< 0.1%
2111
< 0.1%
2021
< 0.1%
2011
< 0.1%
1901
< 0.1%
1891
< 0.1%
1862
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct458
Distinct (%)5.4%
Missing11530
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean91.7742621
Minimum11
Maximum1650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:49.265317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile36
Q153
median70
Q3100
95-th percentile209
Maximum1650
Range1639
Interquartile range (IQR)47

Descriptive statistics

Standard deviation86.9212757
Coefficient of variation (CV)0.9471203986
Kurtosis71.98889947
Mean91.7742621
Median Absolute Deviation (MAD)20
Skewness6.678583052
Sum777328
Variance7555.30817
MonotonicityNot monotonic
2021-11-29T11:24:49.361698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53140
 
0.7%
66139
 
0.7%
57134
 
0.7%
58133
 
0.7%
55133
 
0.7%
56132
 
0.7%
54131
 
0.7%
69126
 
0.6%
63125
 
0.6%
61124
 
0.6%
Other values (448)7153
35.8%
(Missing)11530
57.6%
ValueCountFrequency (%)
112
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
162
 
< 0.1%
184
< 0.1%
194
< 0.1%
203
 
< 0.1%
217
< 0.1%
228
< 0.1%
ValueCountFrequency (%)
16502
< 0.1%
14471
< 0.1%
13661
< 0.1%
12761
< 0.1%
12141
< 0.1%
11341
< 0.1%
11291
< 0.1%
11161
< 0.1%
10721
< 0.1%
10051
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct241
Distinct (%)1.3%
Missing1550
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean8.680187534
Minimum1.07
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:49.465136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.07
5-th percentile7.5
Q18.2
median8.6
Q39
95-th percentile9.7775
Maximum27.9
Range26.83
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation1.253103093
Coefficient of variation (CV)0.1443635968
Kurtosis41.41546674
Mean8.680187534
Median Absolute Deviation (MAD)0.4
Skewness4.233694042
Sum160149.46
Variance1.570267361
MonotonicityNot monotonic
2021-11-29T11:24:49.563859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.51208
 
6.0%
8.61196
 
6.0%
8.71149
 
5.7%
8.31144
 
5.7%
8.81110
 
5.5%
8.41086
 
5.4%
8.91056
 
5.3%
8.2954
 
4.8%
9944
 
4.7%
8.1867
 
4.3%
Other values (231)7736
38.7%
(Missing)1550
 
7.8%
ValueCountFrequency (%)
1.072
< 0.1%
1.083
< 0.1%
1.091
 
< 0.1%
1.12
< 0.1%
1.112
< 0.1%
1.121
 
< 0.1%
1.132
< 0.1%
1.141
 
< 0.1%
1.161
 
< 0.1%
1.172
< 0.1%
ValueCountFrequency (%)
27.91
< 0.1%
271
< 0.1%
25.21
< 0.1%
24.91
< 0.1%
23.71
< 0.1%
22.61
< 0.1%
22.22
< 0.1%
21.21
< 0.1%
20.61
< 0.1%
20.42
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct53
Distinct (%)3.3%
Missing18383
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean106.7959184
Minimum74
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:49.660382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile97
Q1104
median107
Q3110
95-th percentile115
Maximum124
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.408307065
Coefficient of variation (CV)0.05064151465
Kurtosis2.309776643
Mean106.7959184
Median Absolute Deviation (MAD)3
Skewness-0.5865828136
Sum172689
Variance29.24978531
MonotonicityNot monotonic
2021-11-29T11:24:49.758658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107157
 
0.8%
108147
 
0.7%
109135
 
0.7%
106134
 
0.7%
105116
 
0.6%
110112
 
0.6%
10495
 
0.5%
11194
 
0.5%
10387
 
0.4%
10262
 
0.3%
Other values (43)478
 
2.4%
(Missing)18383
91.9%
ValueCountFrequency (%)
741
 
< 0.1%
821
 
< 0.1%
851
 
< 0.1%
863
 
< 0.1%
884
< 0.1%
892
 
< 0.1%
904
< 0.1%
915
< 0.1%
924
< 0.1%
938
< 0.1%
ValueCountFrequency (%)
1247
< 0.1%
1231
 
< 0.1%
1222
 
< 0.1%
1213
 
< 0.1%
1206
 
< 0.1%
1195
 
< 0.1%
1189
< 0.1%
1178
< 0.1%
116.51
 
< 0.1%
11617
0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1177
Distinct (%)6.4%
Missing1588
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean1.714813165
Minimum0.2
Maximum41.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:49.860119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.55
Q10.77
median1
Q31.46
95-th percentile6.3945
Maximum41.9
Range41.7
Interquartile range (IQR)0.69

Descriptive statistics

Standard deviation2.338576173
Coefficient of variation (CV)1.363749836
Kurtosis27.40770534
Mean1.714813165
Median Absolute Deviation (MAD)0.28
Skewness4.488848926
Sum31573.14
Variance5.468938518
MonotonicityNot monotonic
2021-11-29T11:24:49.951753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81261
 
1.3%
0.8241
 
1.2%
0.84240
 
1.2%
0.82238
 
1.2%
0.78226
 
1.1%
0.89224
 
1.1%
0.76219
 
1.1%
0.79219
 
1.1%
0.77219
 
1.1%
0.73217
 
1.1%
Other values (1167)16108
80.5%
(Missing)1588
 
7.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.221
 
< 0.1%
0.241
 
< 0.1%
0.261
 
< 0.1%
0.272
 
< 0.1%
0.281
 
< 0.1%
0.291
 
< 0.1%
0.331
0.2%
0.315
 
< 0.1%
0.325
 
< 0.1%
ValueCountFrequency (%)
41.91
 
< 0.1%
29.861
 
< 0.1%
29.21
 
< 0.1%
254
< 0.1%
24.991
 
< 0.1%
24.031
 
< 0.1%
241
 
< 0.1%
23.831
 
< 0.1%
22.351
 
< 0.1%
22.21
 
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct170
Distinct (%)11.6%
Missing18529
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean0.7670700204
Minimum0.01
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:50.046955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.1
median0.2
Q30.47
95-th percentile2.705
Maximum30
Range29.99
Interquartile range (IQR)0.37

Descriptive statistics

Standard deviation2.200593963
Coefficient of variation (CV)2.868830621
Kurtosis61.76617545
Mean0.7670700204
Median Absolute Deviation (MAD)0.1
Skewness7.136593048
Sum1128.36
Variance4.84261379
MonotonicityNot monotonic
2021-11-29T11:24:50.140683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1429
 
2.1%
0.2275
 
1.4%
0.3128
 
0.6%
0.484
 
0.4%
0.538
 
0.2%
0.633
 
0.2%
120
 
0.1%
0.718
 
0.1%
0.816
 
0.1%
1.114
 
0.1%
Other values (160)416
 
2.1%
(Missing)18529
92.6%
ValueCountFrequency (%)
0.015
 
< 0.1%
0.024
 
< 0.1%
0.035
 
< 0.1%
0.044
 
< 0.1%
0.054
 
< 0.1%
0.066
 
< 0.1%
0.077
 
< 0.1%
0.084
 
< 0.1%
0.0910
 
0.1%
0.1429
2.1%
ValueCountFrequency (%)
301
< 0.1%
23.621
< 0.1%
20.571
< 0.1%
20.551
< 0.1%
202
< 0.1%
19.31
< 0.1%
18.611
< 0.1%
18.51
< 0.1%
16.61
< 0.1%
15.61
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct744
Distinct (%)4.0%
Missing1173
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean170.4204116
Minimum44
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:50.314879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile94
Q1122
median152
Q3195
95-th percentile313
Maximum891
Range847
Interquartile range (IQR)73

Descriptive statistics

Standard deviation72.78074433
Coefficient of variation (CV)0.4270658874
Kurtosis7.229435721
Mean170.4204116
Median Absolute Deviation (MAD)34
Skewness2.086764832
Sum3208505.09
Variance5297.036745
MonotonicityNot monotonic
2021-11-29T11:24:50.403940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147176
 
0.9%
146171
 
0.9%
136169
 
0.8%
133169
 
0.8%
121168
 
0.8%
150164
 
0.8%
115164
 
0.8%
120161
 
0.8%
140160
 
0.8%
134158
 
0.8%
Other values (734)17167
85.8%
(Missing)1173
 
5.9%
ValueCountFrequency (%)
441
 
< 0.1%
511
 
< 0.1%
59.51
 
< 0.1%
611
 
< 0.1%
621
 
< 0.1%
631
 
< 0.1%
663
< 0.1%
671
 
< 0.1%
682
< 0.1%
691
 
< 0.1%
ValueCountFrequency (%)
8911
< 0.1%
8711
< 0.1%
8501
< 0.1%
7881
< 0.1%
7571
< 0.1%
7341
< 0.1%
7221
< 0.1%
7081
< 0.1%
6931
< 0.1%
6921
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct874
Distinct (%)18.4%
Missing15240
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean2.890462185
Minimum0.5
Maximum22.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:50.499766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.88
Q11.34
median1.95
Q33.34
95-th percentile8.201
Maximum22.25
Range21.75
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.675611187
Coefficient of variation (CV)0.9256689818
Kurtosis10.73650928
Mean2.890462185
Median Absolute Deviation (MAD)0.75
Skewness2.871853144
Sum13758.6
Variance7.158895227
MonotonicityNot monotonic
2021-11-29T11:24:50.591323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.437
 
0.2%
1.237
 
0.2%
1.337
 
0.2%
1.135
 
0.2%
1.733
 
0.2%
1.3430
 
0.1%
1.3530
 
0.1%
0.929
 
0.1%
129
 
0.1%
1.4728
 
0.1%
Other values (864)4435
 
22.2%
(Missing)15240
76.2%
ValueCountFrequency (%)
0.54
< 0.1%
0.541
 
< 0.1%
0.551
 
< 0.1%
0.562
 
< 0.1%
0.574
< 0.1%
0.591
 
< 0.1%
0.66
< 0.1%
0.611
 
< 0.1%
0.621
 
< 0.1%
0.634
< 0.1%
ValueCountFrequency (%)
22.251
< 0.1%
21.521
< 0.1%
21.261
< 0.1%
21.211
< 0.1%
19.791
< 0.1%
19.711
< 0.1%
19.661
< 0.1%
19.321
< 0.1%
19.121
< 0.1%
19.021
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct70
Distinct (%)0.4%
Missing3543
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2.148046424
Minimum0.5
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:50.686049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.6
Q11.9
median2.1
Q32.3
95-th percentile2.8
Maximum9.8
Range9.3
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.4110517987
Coefficient of variation (CV)0.1913607612
Kurtosis30.69868694
Mean2.148046424
Median Absolute Deviation (MAD)0.2
Skewness3.084404281
Sum35350.4
Variance0.1689635812
MonotonicityNot monotonic
2021-11-29T11:24:50.778529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22343
11.7%
2.12287
11.4%
2.21991
10.0%
1.91712
8.6%
2.31551
7.8%
1.81258
 
6.3%
2.41091
 
5.5%
2.5811
 
4.1%
1.7754
 
3.8%
2.6511
 
2.6%
Other values (60)2148
10.7%
(Missing)3543
17.7%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.71
 
< 0.1%
0.92
 
< 0.1%
15
 
< 0.1%
1.116
 
0.1%
1.230
 
0.1%
1.365
 
0.3%
1.4126
0.6%
1.451
 
< 0.1%
1.5251
1.3%
ValueCountFrequency (%)
9.81
< 0.1%
9.31
< 0.1%
8.11
< 0.1%
7.91
< 0.1%
7.61
< 0.1%
71
< 0.1%
6.51
< 0.1%
6.41
< 0.1%
6.22
< 0.1%
6.11
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct135
Distinct (%)1.2%
Missing8365
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean3.807700902
Minimum0.6
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:50.872490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile2.2
Q13
median3.6
Q34.3
95-th percentile6.3
Maximum15.5
Range14.9
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.357147494
Coefficient of variation (CV)0.3564217697
Kurtosis6.398993699
Mean3.807700902
Median Absolute Deviation (MAD)0.7
Skewness1.839090252
Sum44302.6
Variance1.841849321
MonotonicityNot monotonic
2021-11-29T11:24:50.972217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4523
 
2.6%
3.5507
 
2.5%
3.3506
 
2.5%
3.2482
 
2.4%
3.6479
 
2.4%
3.8457
 
2.3%
3.1449
 
2.2%
3.7441
 
2.2%
2.9441
 
2.2%
3433
 
2.2%
Other values (125)6917
34.6%
(Missing)8365
41.8%
ValueCountFrequency (%)
0.63
 
< 0.1%
0.72
 
< 0.1%
0.87
 
< 0.1%
0.92
 
< 0.1%
112
0.1%
1.15
 
< 0.1%
1.212
0.1%
1.319
0.1%
1.413
0.1%
1.527
0.1%
ValueCountFrequency (%)
15.51
 
< 0.1%
14.21
 
< 0.1%
12.71
 
< 0.1%
12.61
 
< 0.1%
12.22
 
< 0.1%
1214
0.1%
11.82
 
< 0.1%
11.63
 
< 0.1%
11.51
 
< 0.1%
11.42
 
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct278
Distinct (%)1.5%
Missing1434
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean4.325710438
Minimum2.3
Maximum15.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:51.075705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.5
Q13.9
median4.2
Q34.6
95-th percentile5.5
Maximum15.8
Range13.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.72341582
Coefficient of variation (CV)0.1672363026
Kurtosis15.19444244
Mean4.325710438
Median Absolute Deviation (MAD)0.3
Skewness2.574320567
Sum80311.14
Variance0.5233304487
MonotonicityNot monotonic
2021-11-29T11:24:51.182887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41564
 
7.8%
4.11526
 
7.6%
4.21444
 
7.2%
4.31291
 
6.5%
3.91270
 
6.3%
4.41155
 
5.8%
3.81118
 
5.6%
4.5926
 
4.6%
3.7864
 
4.3%
4.6797
 
4.0%
Other values (268)6611
33.1%
(Missing)1434
 
7.2%
ValueCountFrequency (%)
2.31
 
< 0.1%
2.41
 
< 0.1%
2.53
 
< 0.1%
2.65
 
< 0.1%
2.77
 
< 0.1%
2.811
 
0.1%
2.919
 
0.1%
345
0.2%
3.186
0.4%
3.297
0.5%
ValueCountFrequency (%)
15.81
 
< 0.1%
11.81
 
< 0.1%
11.52
< 0.1%
10.82
< 0.1%
10.751
 
< 0.1%
10.63
< 0.1%
10.42
< 0.1%
10.21
 
< 0.1%
101
 
< 0.1%
9.84
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct198
Distinct (%)2.3%
Missing11522
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean1.372605567
Minimum0.1
Maximum49.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:51.285225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.6
median0.8
Q31.3
95-th percentile3.6
Maximum49.6
Range49.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.636673757
Coefficient of variation (CV)1.920926025
Kurtosis112.3733259
Mean1.372605567
Median Absolute Deviation (MAD)0.3
Skewness9.205650692
Sum11636.95
Variance6.952048501
MonotonicityNot monotonic
2021-11-29T11:24:51.384865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6851
 
4.3%
0.5828
 
4.1%
0.7823
 
4.1%
0.8698
 
3.5%
0.4686
 
3.4%
0.9599
 
3.0%
1499
 
2.5%
1.1384
 
1.9%
0.3376
 
1.9%
1.2319
 
1.6%
Other values (188)2415
 
12.1%
(Missing)11522
57.6%
ValueCountFrequency (%)
0.130
 
0.1%
0.151
 
< 0.1%
0.2147
 
0.7%
0.251
 
< 0.1%
0.3376
1.9%
0.352
 
< 0.1%
0.4686
3.4%
0.451
 
< 0.1%
0.5828
4.1%
0.551
 
< 0.1%
ValueCountFrequency (%)
49.62
< 0.1%
49.21
< 0.1%
45.31
< 0.1%
42.91
< 0.1%
40.91
< 0.1%
38.81
< 0.1%
37.51
< 0.1%
36.51
< 0.1%
34.71
< 0.1%
341
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct1143
Distinct (%)17.4%
Missing13436
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean6.66932663
Minimum0.01
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:51.483212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.03
median0.09
Q31
95-th percentile40
Maximum440
Range439.99
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation25.55908401
Coefficient of variation (CV)3.832333522
Kurtosis72.44230041
Mean6.66932663
Median Absolute Deviation (MAD)0.08
Skewness7.321145882
Sum43777.46
Variance653.2667757
MonotonicityNot monotonic
2021-11-29T11:24:51.576531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011209
 
6.0%
0.03818
 
4.1%
0.02346
 
1.7%
0.04273
 
1.4%
0.05187
 
0.9%
0.06167
 
0.8%
0.07163
 
0.8%
0.08115
 
0.6%
0.1109
 
0.5%
0.0999
 
0.5%
Other values (1133)3078
 
15.4%
(Missing)13436
67.2%
ValueCountFrequency (%)
0.011209
6.0%
0.02346
 
1.7%
0.03818
4.1%
0.04273
 
1.4%
0.05187
 
0.9%
0.06167
 
0.8%
0.07163
 
0.8%
0.08115
 
0.6%
0.0999
 
0.5%
0.1109
 
0.5%
ValueCountFrequency (%)
4402
 
< 0.1%
394.031
 
< 0.1%
381.61
 
< 0.1%
325.311
 
< 0.1%
271.61
 
< 0.1%
243.811
 
< 0.1%
235.881
 
< 0.1%
226.781
 
< 0.1%
20034
0.2%
199.411
 
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct398
Distinct (%)2.2%
Missing1953
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean33.73967031
Minimum9.3
Maximum70.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:51.748670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile24.5
Q129.1
median33.4
Q337.9
95-th percentile43.9
Maximum70.2
Range60.9
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation6.042713362
Coefficient of variation (CV)0.1790981746
Kurtosis-0.04736581214
Mean33.73967031
Median Absolute Deviation (MAD)4.4
Skewness0.3138012684
Sum608899.83
Variance36.51438478
MonotonicityNot monotonic
2021-11-29T11:24:51.842835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34138
 
0.7%
36.1125
 
0.6%
33125
 
0.6%
29.1124
 
0.6%
34.6124
 
0.6%
33.4121
 
0.6%
32.1118
 
0.6%
33.5117
 
0.6%
29117
 
0.6%
35116
 
0.6%
Other values (388)16822
84.1%
(Missing)1953
 
9.8%
ValueCountFrequency (%)
9.31
 
< 0.1%
13.31
 
< 0.1%
14.41
 
< 0.1%
14.61
 
< 0.1%
15.51
 
< 0.1%
161
 
< 0.1%
16.71
 
< 0.1%
16.91
 
< 0.1%
17.41
 
< 0.1%
18.13
< 0.1%
ValueCountFrequency (%)
70.21
 
< 0.1%
653
< 0.1%
621
 
< 0.1%
61.21
 
< 0.1%
60.21
 
< 0.1%
59.11
 
< 0.1%
591
 
< 0.1%
581
 
< 0.1%
57.41
 
< 0.1%
57.31
 
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct207
Distinct (%)1.1%
Missing1941
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean11.11210089
Minimum2.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:51.942588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile8
Q19.5
median10.9
Q312.5
95-th percentile14.8
Maximum30
Range27.4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.168950085
Coefficient of variation (CV)0.1951881203
Kurtosis1.340921459
Mean11.11210089
Median Absolute Deviation (MAD)1.5
Skewness0.6569832784
Sum200673.43
Variance4.704344473
MonotonicityNot monotonic
2021-11-29T11:24:52.035317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.1333
 
1.7%
10.8332
 
1.7%
11.3326
 
1.6%
10.5321
 
1.6%
9.2320
 
1.6%
10.7316
 
1.6%
11.2316
 
1.6%
10.9315
 
1.6%
11.7312
 
1.6%
10.2312
 
1.6%
Other values (197)14856
74.3%
(Missing)1941
 
9.7%
ValueCountFrequency (%)
2.61
< 0.1%
41
< 0.1%
4.31
< 0.1%
4.51
< 0.1%
4.81
< 0.1%
4.91
< 0.1%
5.11
< 0.1%
5.51
< 0.1%
5.62
< 0.1%
5.71
< 0.1%
ValueCountFrequency (%)
301
 
< 0.1%
26.61
 
< 0.1%
251
 
< 0.1%
24.82
< 0.1%
241
 
< 0.1%
23.82
< 0.1%
23.63
< 0.1%
23.41
 
< 0.1%
23.21
 
< 0.1%
22.41
 
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct747
Distinct (%)17.0%
Missing15602
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean44.49657799
Minimum20
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:52.132688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.2
Q128.4
median32
Q340.3
95-th percentile119.7125
Maximum250
Range230
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation39.26047598
Coefficient of variation (CV)0.8823257372
Kurtosis15.02148047
Mean44.49657799
Median Absolute Deviation (MAD)4.7
Skewness3.779902636
Sum195695.95
Variance1541.384974
MonotonicityNot monotonic
2021-11-29T11:24:52.230165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24956
 
0.3%
28.941
 
0.2%
28.839
 
0.2%
30.838
 
0.2%
2836
 
0.2%
27.336
 
0.2%
30.736
 
0.2%
28.636
 
0.2%
29.735
 
0.2%
31.335
 
0.2%
Other values (737)4010
 
20.1%
(Missing)15602
78.0%
ValueCountFrequency (%)
2021
0.1%
20.13
 
< 0.1%
20.31
 
< 0.1%
20.43
 
< 0.1%
20.52
 
< 0.1%
20.62
 
< 0.1%
20.71
 
< 0.1%
20.83
 
< 0.1%
20.95
 
< 0.1%
214
 
< 0.1%
ValueCountFrequency (%)
25010
 
0.1%
249.913
 
0.1%
24956
0.3%
248.71
 
< 0.1%
2481
 
< 0.1%
247.51
 
< 0.1%
2472
 
< 0.1%
246.81
 
< 0.1%
238.11
 
< 0.1%
237.51
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct481
Distinct (%)2.7%
Missing2000
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean11.40393778
Minimum0.1
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:52.328748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4.6
Q17.5
median10.2
Q313.7
95-th percentile21.3
Maximum440
Range439.9
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation8.307665789
Coefficient of variation (CV)0.7284909783
Kurtosis747.5277041
Mean11.40393778
Median Absolute Deviation (MAD)3
Skewness19.03370792
Sum205270.88
Variance69.01731086
MonotonicityNot monotonic
2021-11-29T11:24:52.419704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6202
 
1.0%
8.2196
 
1.0%
8.8195
 
1.0%
7.4194
 
1.0%
8186
 
0.9%
10186
 
0.9%
7.8186
 
0.9%
9.4183
 
0.9%
9.2180
 
0.9%
10.4180
 
0.9%
Other values (471)16112
80.6%
(Missing)2000
 
10.0%
ValueCountFrequency (%)
0.19
< 0.1%
0.23
 
< 0.1%
0.33
 
< 0.1%
0.45
< 0.1%
0.52
 
< 0.1%
0.63
 
< 0.1%
0.81
 
< 0.1%
0.93
 
< 0.1%
11
 
< 0.1%
1.12
 
< 0.1%
ValueCountFrequency (%)
4401
< 0.1%
3871
< 0.1%
2511
< 0.1%
215.31
< 0.1%
199.21
< 0.1%
182.61
< 0.1%
169.71
< 0.1%
166.21
< 0.1%
152.91
< 0.1%
144.91
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct539
Distinct (%)27.7%
Missing18052
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean322.0056468
Minimum35
Maximum1179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:52.512436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile147.35
Q1217
median288
Q3386
95-th percentile636.3
Maximum1179
Range1144
Interquartile range (IQR)169

Descriptive statistics

Standard deviation154.5451557
Coefficient of variation (CV)0.4799454831
Kurtosis3.148556483
Mean322.0056468
Median Absolute Deviation (MAD)80
Skewness1.529860586
Sum627267
Variance23884.20516
MonotonicityNot monotonic
2021-11-29T11:24:52.611587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21720
 
0.1%
20015
 
0.1%
23014
 
0.1%
30513
 
0.1%
23613
 
0.1%
33413
 
0.1%
20813
 
0.1%
28012
 
0.1%
22912
 
0.1%
21512
 
0.1%
Other values (529)1811
 
9.1%
(Missing)18052
90.3%
ValueCountFrequency (%)
351
< 0.1%
581
< 0.1%
612
< 0.1%
621
< 0.1%
651
< 0.1%
701
< 0.1%
711
< 0.1%
761
< 0.1%
812
< 0.1%
861
< 0.1%
ValueCountFrequency (%)
11791
 
< 0.1%
10008
< 0.1%
9661
 
< 0.1%
9542
 
< 0.1%
9451
 
< 0.1%
9321
 
< 0.1%
9191
 
< 0.1%
9121
 
< 0.1%
9011
 
< 0.1%
8971
 
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct651
Distinct (%)3.6%
Missing1992
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean212.581908
Minimum4
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:52.710481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile88
Q1149
median199.5
Q3259
95-th percentile378.65
Maximum2322
Range2318
Interquartile range (IQR)110

Descriptive statistics

Standard deviation96.25496857
Coefficient of variation (CV)0.4527900303
Kurtosis17.39453052
Mean212.581908
Median Absolute Deviation (MAD)54.5
Skewness1.946998427
Sum3828175
Variance9265.018975
MonotonicityNot monotonic
2021-11-29T11:24:52.808438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167112
 
0.6%
206104
 
0.5%
178102
 
0.5%
175102
 
0.5%
204100
 
0.5%
18799
 
0.5%
20299
 
0.5%
17399
 
0.5%
15898
 
0.5%
17698
 
0.5%
Other values (641)16995
85.0%
(Missing)1992
 
10.0%
ValueCountFrequency (%)
42
< 0.1%
53
< 0.1%
61
 
< 0.1%
72
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
113
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
ValueCountFrequency (%)
23221
< 0.1%
11401
< 0.1%
10361
< 0.1%
9841
< 0.1%
9651
< 0.1%
9321
< 0.1%
9071
< 0.1%
8691
< 0.1%
8651
< 0.1%
8541
< 0.1%

Age
Real number (ℝ≥0)

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.6488
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:52.908970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q150
median62
Q372
95-th percentile85
Maximum100
Range86
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.67181022
Coefficient of variation (CV)0.2748910155
Kurtosis-0.1562598942
Mean60.6488
Median Absolute Deviation (MAD)11
Skewness-0.2649819802
Sum1212976
Variance277.949256
MonotonicityNot monotonic
2021-11-29T11:24:53.001394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67572
 
2.9%
68539
 
2.7%
66512
 
2.6%
65510
 
2.5%
61498
 
2.5%
69495
 
2.5%
71481
 
2.4%
62477
 
2.4%
63470
 
2.4%
70467
 
2.3%
Other values (67)14979
74.9%
ValueCountFrequency (%)
142
 
< 0.1%
152
 
< 0.1%
165
 
< 0.1%
1713
 
0.1%
1832
 
0.2%
1954
0.3%
2065
0.3%
2199
0.5%
2259
0.3%
2377
0.4%
ValueCountFrequency (%)
100392
2.0%
89111
 
0.6%
88138
 
0.7%
87145
 
0.7%
86187
0.9%
85187
0.9%
84206
1.0%
83247
1.2%
82242
1.2%
81224
1.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
10732 
0
9268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Length

2021-11-29T11:24:53.165495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:53.217740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring characters

ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110732
53.7%
09268
46.3%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
0.0
6982 
1.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06982
34.9%
1.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:24:53.271325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:53.322391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.06982
50.2%
1.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020887
75.1%
16923
 
24.9%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
1.0
6982 
0.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06982
34.9%
0.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:24:53.376468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:53.427433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06982
50.2%
0.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020828
74.9%
16982
 
25.1%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

HospAdmTime
Real number (ℝ)

ZEROS

Distinct7975
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-55.072586
Minimum-5366.86
Maximum0
Zeros1145
Zeros (%)5.7%
Negative18855
Negative (%)94.3%
Memory size156.4 KiB
2021-11-29T11:24:53.487994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5366.86
5-th percentile-244.829
Q1-52.3525
median-8.57
Q3-3.38
95-th percentile0
Maximum0
Range5366.86
Interquartile range (IQR)48.9725

Descriptive statistics

Standard deviation135.5956936
Coefficient of variation (CV)-2.462126867
Kurtosis241.4449376
Mean-55.072586
Median Absolute Deviation (MAD)8.52
Skewness-10.8324003
Sum-1101451.72
Variance18386.19213
MonotonicityNot monotonic
2021-11-29T11:24:53.591698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01145
 
5.7%
-0.02250
 
1.2%
-0.03197
 
1.0%
-0.01179
 
0.9%
-0.04136
 
0.7%
-0.05122
 
0.6%
-0.06105
 
0.5%
-0.0794
 
0.5%
-0.0976
 
0.4%
-0.0859
 
0.3%
Other values (7965)17637
88.2%
ValueCountFrequency (%)
-5366.861
< 0.1%
-3397.641
< 0.1%
-3342.341
< 0.1%
-3189.391
< 0.1%
-3112.121
< 0.1%
-2929.371
< 0.1%
-2842.111
< 0.1%
-2667.341
< 0.1%
-2384.781
< 0.1%
-2382.341
< 0.1%
ValueCountFrequency (%)
01145
5.7%
-0.01179
 
0.9%
-0.02250
 
1.2%
-0.03197
 
1.0%
-0.04136
 
0.7%
-0.05122
 
0.6%
-0.06105
 
0.5%
-0.0794
 
0.5%
-0.0859
 
0.3%
-0.0976
 
0.4%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.2332
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:53.694245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.2718112
Coefficient of variation (CV)0.6086807069
Kurtosis44.09947372
Mean38.2332
Median Absolute Deviation (MAD)12
Skewness4.971265249
Sum764664
Variance541.5771966
MonotonicityNot monotonic
2021-11-29T11:24:53.793227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39684
 
3.4%
40671
 
3.4%
36669
 
3.3%
38667
 
3.3%
41638
 
3.2%
43611
 
3.1%
42597
 
3.0%
37580
 
2.9%
21563
 
2.8%
44558
 
2.8%
Other values (219)13762
68.8%
ValueCountFrequency (%)
8193
1.0%
9105
 
0.5%
10116
 
0.6%
11111
 
0.6%
12151
0.8%
13190
0.9%
14214
1.1%
15319
1.6%
16330
1.7%
17360
1.8%
ValueCountFrequency (%)
3367
< 0.1%
3351
 
< 0.1%
3341
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%
3081
 
< 0.1%

SepsisLabel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0
18858 
1
 
1142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Length

2021-11-29T11:24:53.890597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:53.943003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring characters

ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0
18858 
1
 
1142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Length

2021-11-29T11:24:53.997414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:54.049709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring characters

ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018858
94.3%
11142
 
5.7%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:54.111129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:54.210172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Interactions

2021-11-29T11:24:42.684300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.315500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.415042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.503283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.589918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.682837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.768144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.855003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:39.941852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.027195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.110489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.198372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.278340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.371139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.454709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.538301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.623353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.710753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.795816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:40.886095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.043987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.129149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.212074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.301620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.383797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.467939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.552101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.644059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.733480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.820392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.905841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:41.993582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.077659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.162026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.244565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.334646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.420276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.504350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:24:42.593590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:24:54.353684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:24:54.760257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:24:55.092850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:24:55.371327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:24:42.995222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:24:44.041515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:24:44.671730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:24:45.455727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
0100001107.099.037.0123.089.072.023.5NaNNaNNaNNaNNaNNaNNaNNaN30.0NaN7.8NaN1.50NaN233.0NaN2.1NaN4.2NaNNaN35.311.3NaN10.8NaN170.07311.00.0-214.64240024
110000271.5100.037.9157.095.071.026.039.0NaNNaNNaNNaNNaNNaNNaN17.0NaN8.2NaN0.84NaN222.0NaN2.33.65.2NaN3.7031.411.1NaN13.2NaN85.08310.01.0-123.17250025
2100003135.0100.038.2168.0119.0105.033.0NaNNaNNaNNaNNaNNaNNaN6182.036.0347.08.4NaN1.923.4190.0NaN2.44.45.04.30.7234.411.4NaN15.5NaN337.0481NaNNaN-2.83430043
3100004130.0100.038.0160.0120.0104.017.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6711.00.0-1.93590059
4100005130.0100.038.9184.595.084.040.0NaNNaNNaN0.47.5524.395.640.063.0231.08.9NaN1.28NaN162.02.842.14.55.90.60.0426.59.035.317.3NaN421.05011.00.0-3.27520052
510000687.0100.037.0161.5111.591.021.0NaNNaN25.0NaNNaN42.098.838.017.051.09.1NaN0.87NaN306.0NaN2.35.14.91.1NaN50.316.724.28.8NaN172.0501NaNNaN0.00520047
6100007109.0100.037.3168.0112.091.022.0NaNNaNNaNNaNNaNNaNNaN34.017.0112.08.5NaN0.410.1141.0NaN1.74.04.00.5NaN26.48.426.67.0NaN403.0421NaNNaN-1145.97370037
7100008104.0100.037.8208.0115.084.528.042.5NaNNaN0.47.4350.0NaNNaN51.0NaN9.1105.03.82NaN219.02.002.55.66.3NaNNaN29.59.8NaN12.9NaN134.06510.01.0-211.64500050
810000988.0100.036.8154.097.584.018.5NaNNaNNaNNaNNaNNaNNaNNaN12.0NaN9.3NaN0.71NaN103.0NaN1.9NaN3.9NaNNaN36.211.8NaN5.7NaN201.0820NaNNaN-128.39300030
9100010105.0100.037.2171.0117.099.019.0NaNNaNNaNNaNNaNNaNNaNNaN34.0NaN9.0NaN1.77NaN417.0NaN2.24.14.1NaNNaNNaNNaNNaNNaNNaNNaN3201.00.0-8.13160016

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
1999011999184.0100.037.3172.0112.070.031.540.0NaNNaNNaNNaNNaNNaNNaN14.0NaN7.5NaN0.79NaN155.0NaN1.6NaN4.1NaNNaN26.38.4NaN6.4NaN96.08100.01.0-66.13250025
19991119992112.0100.037.5213.0153.0111.029.0NaNNaNNaNNaNNaNNaNNaNNaN37.0NaN9.2NaN9.93NaN143.0NaN1.86.64.5NaN0.4130.29.5NaN2.8NaN198.04511.00.0-4.55410041
1999211999390.598.037.2150.5105.086.020.0NaNNaNNaNNaNNaNNaNNaNNaN15.0NaN8.3NaN1.01NaN132.0NaN2.02.64.1NaN0.0142.014.9NaN12.3NaN175.0651NaNNaN-3.53210021
1999311999482.0100.037.9146.089.566.026.042.0NaNNaN0.57.440.098.7NaN18.0NaN8.4NaN1.13NaN155.07.212.23.64.8NaNNaN30.310.3NaN11.4NaN71.07110.01.0-29.57420042
1999411999583.098.036.3175.0124.097.028.0NaNNaNNaNNaNNaNNaNNaNNaN9.0NaN8.8NaN0.81NaN117.0NaN2.03.03.5NaNNaN39.213.1NaN7.0289.0154.07610.01.0-14.90420042
19995119996124.0100.037.0164.0105.0109.023.0NaNNaNNaNNaNNaNNaNNaN849.08.0259.08.8NaN0.58NaN160.0NaN2.3NaN4.03.30.0142.713.8NaN12.6NaN238.0840NaNNaN-6.69480048
1999611999780.0100.037.3156.0193.590.026.045.0NaNNaNNaNNaNNaNNaN24.06.0116.017.8NaN0.850.1106.0NaN3.23.13.60.71.0949.416.138.210.8NaN201.0301NaNNaN-0.02250025
19997119998103.0100.037.6205.5158.5130.526.0NaNNaNNaNNaNNaNNaNNaN9.061.068.08.3NaN8.77NaN91.0NaN1.94.14.30.2NaN30.29.6NaN13.6NaN225.06001.00.0-53.64490049
19998119999106.0100.037.9164.0114.082.027.0NaNNaNNaNNaNNaNNaNNaN34.029.050.08.5NaN1.04NaN111.0NaNNaNNaN3.41.1NaN26.58.7NaN11.4NaN272.08401.00.0-10.74200020
19999120000101.099.037.0168.0119.084.020.0NaNNaNNaNNaNNaNNaNNaN18.011.075.09.3NaN0.540.1242.0NaN2.24.03.60.9NaN37.612.029.16.8NaN230.0620NaNNaN0.00350035